Descriptive statistics :measures of location, dispersion, and skewness. Probability. Random variables. Normal and Binomial probability distributions. Sampling distribution of the mean. Estimation. Testing hypotheses. Regression and correlation. Applications using statistical packages. (It cannot to be taken for credit with Stat 319 or SE 205).

Data description: Frequency table, histogram, measures of central tendency and variability, scatter diagram and correlation. Probability theory; sampling; probability distributions; point and confidence interval estimation; application for managerial decisions. A statistical package will be used. The course is not open for credit to Statistics or Mathematics Majors, and can not be taken fore credit with SE 205, STAT 201 and STAT 319

Hypothesis testing for means and variances; index number and time series; simple linear regression and correlation analysis; multiple regression analysis; the chi-square and F distributions and their applications. A statistical package will be used. The course is not open for credit to Statistics or Mathematics Majors, and can not be taken fore credit with SE 205, STAT 201 and STAT 319

Basic classical models of probability. Set functions. Axiomatic definition of probability. Conditional probability and Bayes ’ theorem. . Random variables and their types. Distributions, moments, and moment generating functions. Special discrete and continuous distributions. Random vectors and their distributions. Marginal and conditional distributions. Independent random variables. Functions of random variables. Sums of independent random variables. Weak law of large numbers and the central limit theorem.

Random sampling and the sampling distributions :t, chi-square, and F. Order Statistics. Methods of estimation :maximum likelihood and moments. Properties of a good estimator :unbiasedness, consistency, efficiency, sufficiency, and approximate normality. Testing of simple hypotheses, the Neyman-Pearson lemma. Testing composite hypotheses, uniformly most powerful and likelihood ratio tests. Bayesian Statistics.

Simple linear regression :The least squares method, parameter estimation, confidence intervals, tests of hypotheses and model adequacy checking. Multiple linear regression, including estimation of parameters, confidence intervals, tests of hypotheses and prediction. Model adequacy checking and multicollinearity. Polynomial regression. Variable selection and model building.

Presentation and interpretation of data, elementary probability concepts, random variables and probability distributions, binomial, Poisson, exponential, Weibull, normal and lognormal random variables. Estimation, tests of hypotheses for the one sample problem. Simple and multiple linear regression, application to engineering problems. The lab session will be devoted to problem solving using statistics software. (The course is not open for credit to Statistics or Mathematics Majors and cannot be taken for credit with SE 205 or STAT 201).

How control charts work. Control chart methods for attributes and variables. Process- control chart techniques. Process-capability analysis. Acceptance-sampling by attributes and variables. (It cannot be taken for credit with SE 320).

One sample problem, the sign, and Wilcoxon signed rank tests. Two-Sample problem, Wilcoxon rank sum and Mann-Whitney tests. Kruskal-Wallis test for one-way layout. Friedman test for randomized block design. Run test for randomness. Goodness of fit tests.

Review for descriptive statistics, estimation, and testing hypotheses. Simple linear regression. One way analysis of variance. Multiple regression. Randomized block designs. Factorial experiments. Random and mixed effect models. (It cannot be taken for credit with STAT 310 and or STAT 430).

Scope of demography. Vital events. Demographic survey. History of world population and distribution. Demographic transition. Fertility and its measures. Mortality and its measures. Direct and indirect standardization. The life table. Construction of a life table. Stationary population. Stable population. Migration. Theories of migration. Consequences of migration. Population estimates and projections.

Problem solving and decision making. Linear programming :formulation, the graphical method, the simplex method, sensitivity analysis, and duality. Transportation and assignment problem. Integer programming. Project scheduling PERT/CPM. (It cannot be taken for credit with SE 303).

Concept of data collection. Sample surveys, finite and infinite populations, execution and analysis of samples. Basic sampling designs :simple, stratified, systematic, cluster, two-stage cluster. Methods of estimation of population means, proportions, totals, sizes, variances, standard errors, ratio, and regression.

2x2 contingency tables, two-way contingency tables, three-way and higher dimensional contingency tables. Loglinear models for contingency tables. Logistic regression. Building and applying loglinear models.

Students are required to spend one summer working in industry prior to the term in which they expect to graduate. Students are required to submit a report and make a presentation on their summer training experience and the knowledge gained.

Basic classes of Stochastic processes. Poisson and renewal processes with applications in simple queuing systems. Discrete and continuous time Markov chains. Birth-Death and Yule processes. Branching models of population growth and physical processes.

Importance of statistical design of experiments. Single-factor and multifactor analysis of variance. Factorial designs. Randomized blocks. Nested designs. Latin squares. Confounding and 2-level fractional factorials. Analysis of covariance.

Review of multiple regression. The general linear model. Quadratic forms. Gauss- Markov theorem. Multivariate normal distribution. Computational aspects. Full rank models. Models not of full rank. Computer applications.

Introduction to multivariate analysis. Multivariate normal distribution theory. Distribution of the sum of product matrix. Inference about the parameters of the multivariate normal distribution. Comparison of means. Linear models. Principal components. Factor analysis. Classification and discrimination techniques.

Examples of simple time series. Stationary time series and autocorrelation. Autoregressive moving average processes. Modeling and forecasting with ARMA processes. Maximum likelihood and least squares estimator. Nonstationary time series.

Inventory models. Waiting line models. Decision Analysis. Multicriteria decision problem. Markov process. Dynamic programming. Calculus-based Procedures. (It cannot be taken for credit with SE 421).

This course is designed to draw upon various components of the undergraduate curriculum. The project could be in the area of data analysis, sampling survey, experimental design, regression analysis, multivariate data analysis, time series and etc. A report is essential for course completion.

Life tables, graph and related procedures. Single samples :complete or Type II censored data and Type I censored data for Exponential, Weibull, Gamma and other distributions. Parametric regression for Exponential, Weibull and Gamma distributions. Distributions- free methods for proportional hazard and related regression models.

Variable contents. Open for senior students interested in studying an advanced topic in statistics with a departmental faculty member.